Machine learning modeling using social graph signals

ABSTRACT

Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.15/725,075, filed on Oct. 4, 2017, which claims the benefit of priorityto U.S. Provisional Application Ser. No. 62/535,588, filed on Jul. 21,2017, each of which are hereby incorporated by reference herein in theirentireties.

BACKGROUND

Machine learning technology is being developed and utilized for avariety of use cases such as fraud prevention, healthcare trends forimproving diagnoses and treatment, making transportation routes moreefficient, analyzing sensor data to increase efficiency for utilities,personalizing user experiences online, and so forth. In particular,machine learning technology is starting to be utilized to help entitiesexpand a particular audience for targeting content to users.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and should not be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 is a block diagram illustrating a hybrid learning model,according to some example embodiments.

FIG. 3 is a flow chart illustrating aspects of a method, according tosome example embodiments, for processing a lookalike request.

FIG. 4 is a block diagram illustrating a hybrid learning model,according to some example embodiments.

FIG. 5 is a flow chart illustrating aspects of a method, according tosome example embodiments, for processing a lookalike request.

FIG. 6 is a flow chart illustrating aspects of a method, according tosome example embodiments, for processing a lookalike request.

FIG. 7 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network,according to some example embodiments.

FIG. 8 is block diagram illustrating further details regarding amessaging system, according to some example embodiments.

FIG. 9 is a schematic diagram illustrating data which may be stored inthe database of the messaging server system, according to some exampleembodiments.

FIG. 10 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 11 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 12 illustrates a diagrammatic representation of a machine, in theform of a computer system, within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

DETAILED DESCRIPTION

Systems and methods described herein relate to machine learningtechnology for lookalike modeling to expand a list of users withdesirable characteristics to a larger list of users which may havesimilar characteristics. For example, a car company may identify seventhousand profitable customers and want to target content to sevenmillion people who are similar to the seven thousand profitablecustomers. Example embodiments utilize rich social network data to buildand train a model to generate a list of similar users.

Example embodiments describe hybrid lookalike modeling systems andmethods using social graph data to generate a larger group of lookalikeusers based on a seed segment. In one example, a server computer systemmay receive a request for lookalike data. The request for lookalike datamay comprise seed data which the server computer system will use togenerate an expanded list of users with desirable characteristics. Theserver computer system will generate sample data from the seed data andfrom user data for a plurality of users, to use in a lookalike modeltraining. The user data for the plurality of users may be the users fromwhich the server computer system will generate the expanded list ofusers with desirable characteristics. The server computer system may beassociated with a messaging system or a social networking system.

The server computer system will capture a snapshot of social graph datafor a plurality of users and compute social graph features based on theseed data and the user data for the plurality of users. The servercomputer system will train a lookalike model based on the sample data,user profile features for the plurality of users, and the computedsocial graph features to generate a trained lookalike model. The servercomputer system will generate a lookalike score for each user of theplurality of users in the user data using the trained lookalike model,and generate a list comprising a unique identifier for each user of theplurality of users and an associated lookalike score for a uniqueidentifier. This list may be returned to the requester or used toprovide content associated with the requester to users in the generatedlist.

FIG. 1 is a block diagram illustrating a networked system 100, accordingto some example embodiments. The system 100 may include one or moreclient devices such as client device 110. The client device 110 maycomprise, but is not limited to, a mobile phone, desktop computer,laptop, portable digital assistants (PDA), smart phone, tablet,Ultrabook, netbook, laptop, multi-processor system, microprocessor-basedor programmable consumer electronic, game console, set-top box, computerin a vehicle, or any other communication device that a user may utilizeto access the networked system 100. In some embodiments, the clientdevice 110 may comprise a display module (not shown) to displayinformation (e.g., in the form of user interfaces). In furtherembodiments, the client device 110 may comprise one or more of touchscreens, accelerometers, gyroscopes, cameras, microphones, globalpositioning system (GPS) devices, and so forth. The client device 110may be a device of a user that is used to create or generate queries.

One or more users 106 may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the user106 may not be part of the system 100, but may interact with the system100 via the client device 110 or other means. For instance, the user 106may provide input (e.g., touch screen input or alphanumeric input) tothe client device 110, and the input may be communicated to otherentities in the system 100 (e.g., third party servers 130, server system102, etc.) via the network 104. In this instance, the other entities inthe system 100, in response to receiving the input from the user 106,may communicate information to the client device 110 via the network 104to be presented to the user 106. In this way, the user 106 may interactwith the various entities in the system 100 using the client device 110.

The system 100 may further include a network 104. One or more portionsof network 104 may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe public switched telephone network (PSTN), a cellular telephonenetwork, a wireless network, a WiFi network, a WiMax network, anothertype of network, or a combination of two or more such networks.

The client device 110 may access the various data and applicationsprovided by other entities in the system 100 via web client 112 (e.g., abrowser, such as the Internet Explorer® browser developed by Microsoft®Corporation of Redmond, Washington State) or one or more clientapplications 114. The client device 110 may include one or more clientapplications 114 (also referred to as “apps”) such as, but not limitedto, a web browser, messaging application, electronic mail (email)application, an e-commerce site application, a mapping or locationapplication, and the like.

In some embodiments, one or more client applications 114 may be includedin a given one of the client devices 110, and configured to locallyprovide the user interface and at least some of the functionalities,with the client application 114 configured to communicate with otherentities in the system 100 (e.g., third party servers 130, server system102, etc.), on an as needed basis, for data and/or processingcapabilities not locally available (e.g., to process user queries, toauthenticate a user 106, to verify a method of payment, etc.).Conversely, one or more applications 114 may not be included in theclient device 110, and then the client device 110 may use its webbrowser to access the one or more applications hosted on other entitiesin the system 100 (e.g., third party servers 130, server system 102,etc.).

A server system 102 may provide server-side functionality via thenetwork 104 (e.g., the Internet or wide area network (WAN)) to one ormore third party servers 130 and/or one or more client devices 110. Theserver system 102 may include an application program interface (API)server 120, a web server 122, and a lookalike (LAL) modeling server 124,which may be communicatively coupled with one or more databases 126.

The one or more databases 126 may be storage devices that storelookalike modeling data, user data, social graph data, seed data, andother data. The one or more databases 126 may further store informationrelated to third party servers 130, third party applications 132, clientdevices 110, client applications 114, users 106, and so forth. The oneor more databases 126 may include cloud-based storage.

The server system 102 may be a cloud computing environment, according tosome example embodiments. The server system 102, and any serversassociated with the server system 102, may be associated with acloud-based application, in one example embodiment.

The lookalike modeling server 124 may provide back-end support forthird-party applications 132 and client applications 114, which mayinclude cloud-based applications. In one embodiment, the lookalikemodeling server 124 may receive requests from third party servers orclient devices, process the requests to generate lookalike models,generate responses to the requests, and so forth.

The system 100 may further include one or more third party servers 130.The one or more third party servers 130 may include one or more thirdparty application(s) 132. The one or more third party application(s)132, executing on third party server(s) 130, may interact with theserver system 102 via API server 120 via a programmatic interfaceprovided by the API server 120. For example, one or more the third partyapplications 132 may request and utilize information from the serversystem 102 via the API server 120 to support one or more features orfunctions on a website hosted by the third party or an applicationhosted by the third party. The third party website or application 132,for example, may provide functionality that is supported by relevantfunctionality and data in the server system 102.

FIG. 2 is a block diagram illustrating a hybrid learning modeling system200, according to some example embodiments. The hybrid learning modelingsystem 200 shown in FIG. 2 incorporates additional social graph inducedfeatures (also referred to as social network features) with a seedsegment into a lookalike model. The social graph induced features areused as training data to compute new features in the lookalike model andthen used as part of the lookalike model. The social graph inducedfeatures are a set of features that are computed with respect to seedusers (e.g., training data) per lookalike segment (e.g., seed data), andare based on or rely upon the social graph structure.

There are a number of aspects that separate social graph features fromcommonly used features in models. For example, features used in machinelearning models are typically trying to capture similarity of users'interests or behaviors to each other, whereas the social graph inducedfeatures are trying to capture the influence of users over each other.Moreover, some features may encapsulate both interest and/or behavior aswell as influence over others. Additionally, the canonical features usedin machine learning models are usually universal in the sense that thesame feature for the same user can be used in other machine learningmodels and campaigns as long it is needed and it is oblivious oftraining data. This is despite the fact that some feature values may bestale or expired and should be refreshed after a certain period of time.Social graph induced features, on the other hand, are computed based onthe training data (e.g., seed users) and differ for each run of themachine learning model.

Social graph induced features may include features measuring a user'sstructural position in a social graph with respect to the seed users,such as, for instance, the (e.g., normalized) number of links (e.g.,friendship or followership or followeeship) of a user to the seed users.The social graph induced features can be any other graph structuralproperty or measure based on graph entities such as cliques, bridges,(directed) paths, and so forth.

Social graph induced features measure the influence or engagement ofseed users on users given the social graph structure and dynamics (e.g.,the number/time-length of media collections or messages of seed usersviewed, received by, or sent to a user). The social graph inducedfeatures are computed for each user and the additional features areadded to a feature vector. The new feature vector is used to train thelookalike model. The lookalike model thus not only captures the usersengagement with organic content and ad content, but also captures theusers' social interactions with the seed users.

In one example, the hybrid learning modeling system 200 is part of theserver system 102. For example, in the server system 102, the hybridlearning modeling system 200 may be executed by the LAL modeling server124. While the LAL modeling server 124 is shown as one server in FIG. 1,it is understood that the LAL modeling server 124 may comprise multipleservers, processors, or other computing devices. Accordingly, the hybridlearning modeling system 200 may be implemented and executed using oneor more computing devices or processors. Moreover, various databases areshown in FIG. 2, however, it is understood that various data describedherein may be stored in a single database or in multiple databases, orother form of data stores.

The system 200 includes a lookalike (LAL) request module 202 configuredto receive a lookalike request from a computing device. For example, theLAL request module 202 may receive a plurality of LAL requests from aplurality of computing devices associated with third party entities(e.g., via third party servers 130) or via client devices 110. The LALrequest may comprise various information. In one example, the LALrequest comprises seed data. The LAL request module 202 stores seed dataincluded in the request in one or more databases 126C. The LAL requestmodule 202 communicates with a user profile data generation module 204,a sample generation module 206, and a social graph snapshot module 208.

The user profile data generation module 204 processes user profile datato generate user profile features, which are stored in one or moredatabases 126A. The sample generation module 206 generates sample datafrom seed data stored in database 126C and from user data, as describedbelow. The social graph snapshot module 208 captures a current snapshotof social graph data and stores the snapshot of social graph data in oneor more databases 126F. The social graph feature generation module 210generates features using the seed data and social graph snapshot dataand stores the social graph features in one or more databases 126D.

The train LAL model module 212 utilizes model configuration data 126G todetermine which machine learning model(s), of a plurality of machinelearning models, to use to train the LAL model. Examples of machinelearning models include supervised learning models such as logisticregression and tree-based modeling, a form of hybrid learning modeling,and other machine learning models. For example, the train LAL modelmodule 212 may determine, based on the request, based on a defaultmodel, or other factor(s), which machine learning model or combinationof machine learning models should be utilized to train the LAL model andto process the request.

The train LAL model module 212 may utilize various data to train the LALmodel, such as user profile features, sample data, social graphfeatures, and so forth. The trained LAL model is then used by thepredict module 214 to generate LAL results for the request, and the LALresults may be stored in one or more databases 126E.

FIG. 3 is a flow chart illustrating aspects of a method 300, accordingto some example embodiments, for processing an LAL request. Forillustrative purposes, method 300 is described with respect to thenetworked system 100 of FIG. 1 and the block diagram of FIG. 2. It is tobe understood that method 300 may be practiced with other systemconfigurations in other embodiments.

In operation 302, a server computer system (e.g., server system 102 viaLAL modeling server 124 and LAL request module 202), receives a requestfor lookalike data. The server computer system may receive the requestvia API server 120, web server 122, or directly via LAL modeling server124. For example, a client device 110 associated with an entity (e.g.,company, organization, individual, etc.) may send the request to theserver system 102 directly or via a third party server 130.

The request may include various information. In one example, the requestcomprises seed data that comprises at least a plurality of useridentifiers. The plurality of user identifiers may represent users withdesirable characteristics and will be used to expand to a larger list ofusers which may have similar characteristics. Some examples of useridentifiers may include email addresses, mobile phone numbers, or othermeans to uniquely identify a particular user. A user may have more thanone user identifier.

The request may comprise other information, such as a size for thelookalike data. For example, the request may include a small size to geta very targeted number of similar users (e.g., 500, 1000, etc.), alarger size to get a larger amount of users (e.g., 10,000, 5 million,etc.), and so forth. The request may also include filtering parameters,to be used for filtering out certain users based on particularcharacteristics. For example, a car company may want to filter out usersunder a certain age (e.g., under 21, under 30, etc.) or may want tofilter out users who have recently purchased a car, and so forth. Therequest may also comprise particular campaign goals of the entity, orother metrics or optimization goals.

After receiving the request, the server computer system may store thedata from the request in one or more databases 126. For example, theserver computer system may store the seed data in seed data database126C. The server computer system may store the seed data as it wasreceived, or it may perform some processing on the data. For example,the server computer system may have access to user data for a pluralityof users. In one example, the server computer system has user datastored in one or more database 126 from users of the server system 102.In another example, the server computer system accesses user data for aplurality of users from third party sources of data. In yet anotherexample, the server computer system may access and/or store user datafrom users of the server system 102 and user data from one or more thirdparty sources of data. The server computer system may compare the seeddata received in the request for lookalike data to the user data for theplurality of users, to determine which users in the seed data are alsousers in the user data (e.g., to determine which users in the seed datamatch users in the user data). The server computer system may only storethe users of the seed data that match users in the user data in the seeddata database 126C to use in processing the request for lookalike data(e.g., the users in the seed data that are not matched to users in theuser data may be discarded). The server computer system wouldaccordingly use the subset of the original seed data that is stored inthe seed data database 126C in training the model and processing therequest.

The server computer system may utilize user profile data in processingthe lookalike request. For example, the server computer system maycapture a snapshot of user profile data for a plurality of users, for aparticular point in time (e.g., a point in time upon receiving therequest for lookalike data, or before or after receiving the request forlookalike data). The server computer system may process user profiledata to generate user profile features for each user. The servercomputer system may store the user profile features in one or moredatabases 126A.

Some examples of user profile features may include demographics such asage, gender, education level, income level, marital status, occupation,number of children, and so forth. Other user profile features mayinclude activities associated with the user, such as engagement withdifferent social network channels, messaging activities, image capturingactivities (e.g., photos and video), ad engagement, organic contentengagement, interest groups, and so forth. Yet other features mayinclude location, user preferences, and the like. There may be just afew features of interest to the system or a request, or there may behundreds of user features of interest or relevant to the request. A listof relevant features may be predetermined by the server computer system,or may be determined from the request for the lookalike data. Each usermay have different values for each feature. Since many of these userfeatures may not change (e.g., age, gender) or change as frequently(e.g., occupation, education level), computing user profile features maynot need to be recomputed for each request for lookalike data.

In operation 304, the server computer system generates sample data. Forexample, the server computer system may generate sample data from theseed data and from the user data for the plurality of users, to use inlookalike model training. The sample data may include a positive datasample (e.g., positive training data) and a negative data sample (e.g.,negative training data). The positive data sample may be generated fromthe seed data. For example, the positive data sample may comprise all ofthe seed data or a subset of the seed data. The negative data sample maybe generated from user data of the plurality of users.

In one example, a random sample of users (e.g., excluding users from thesample data) may be generated as the negative data sample. In anotherexample, negative sample data may be generated from the user data of theplurality of users based on certain criteria or factors (e.g., the userdoes not like the products sold by the entity associated with therequester, etc.). Other methods for generating negative sample data maybe used in addition, or in the alternative. In one example, the negativedata sample may be the same size as the positive data sample.

In operation 306, the server computer system captures a snapshot ofsocial graph data for the plurality of users. The social graph data maybe data generated by the server system 102 (e.g., the server system 102may comprise a social networking system, messaging system, etc.) and/orthe social graph data may be generated by a third party and accessed bythe server system 102. Social graph data may comprise a graph for eachuser. The graph for each user comprises other users, to which each useris connected, and how they are connected (e.g., directly related asfriends, a friend of a friend, etc.). Social graph data may furthercomprise data such as which other users each user is following, whichother users are following a particular user, how many times a user readsanother user's messages or views another user's media collections (e.g.,stories or galleries), which users have collaborated on the same mediacollection, and so forth. Since connections and interactions with otheruses are constantly changing, a snapshot of the social graph data forthe plurality of users for a given timeframe is captured to use incomputing and generating social graph features. The snapshot of thesocial graph may be stored in one or more databases 126F.

In operation 308, the server computer system computes social graphfeatures based on the seed data and the user data for the plurality ofusers. Since the social graph features change constantly (e.g., newconnections added or deleted, new interactions with connections areconstantly occurring, etc.), the social graph features may be capturedand computed for each lookalike request.

To compute social graph features, the server computer system may analyzethe social graph data (e.g., snapshot of social graph) to determineconnections between users in the user data and seed users (e.g., how isa particular user connected to one or more seed user), interactionsbetween users and seed users, and so forth. For example, one user mayhave connections to three seed users, a second user may be following twostories for a particular seed user, a third user may have no connectionsto any of the seed users, a fourth user may have similar interests toseveral of the seed users, and so forth. From this analysis, the servercomputer system determines additional features to be used in trainingthe lookalike model, and determines values for each feature for eachuser of the plurality of users.

Using a simple example, social graph features may include a firstfeature (f1) number_of_one_hop_folloees_in_seeds, a second feature (f2)log(number_of_two_hop_folloees_in_seeds) and a third feature (f3)number_of_5_clique_friends_involving_current_user_and_seeds. For user u:

-   -   number_of_one_hop_folloees_in_seeds:=Number of seeds that u is        following.    -   log(number_of_two_hop_folloees_in_seeds):=log of the number of        seeds that are followed by u indirectly (e.g., if S is the set        of users that u is following, and T the set of seeds that have        at least one follower in S, then this feature value is the size        of T). It is noted that that in general computing features may        need some caution when computed for seeds themselves to avoid        biasness towards seed users.    -   number_of_5_clique_friends_involving_current_user_and_seeds:=number        of groups of 5 (u and 4 other seeds) that are all friends of        each other    -   Thus, for users A, B, C example values of (f1, f2, f3) may        include:    -   A: (5, 2.67, 5)    -   B: (21, 4.4, 3)    -   C: (0, 0.32, 0)

In operation 310, the server computer system trains a lookalike modelbased on the sample data, the user profile features for the plurality ofusers, and the computed social graph features to generate a trainedlookalike model. The lookalike model may be trained based on modelingdata, as explained above, to calculate the weights or coefficients foreach feature. This calculation is then saved as the trained lookalikemodel.

In operation 312, the server computer system generates a lookalike scorefor each user of the plurality of users in the user data using thetrained lookalike model. For example, for each user of the plurality ofusers, the server computer system uses the trained lookalike model toanalyze the values for each of the features associated with the user andgenerate a score (e.g., between 0 and 1) using the calculated weights orcoefficients for each feature.

In operation 314, the server computer system generates a list comprisinga unique identifier for each user of the plurality of users and anassociated lookalike score (e.g., between 0 and 1) for each uniqueidentifier.

After generating the list of users and associated lookalike scores, thelist may be further culled based on exclusions or constraints from theentity associated with the request. For example, the request may alsocomprise exclusions, constraints, as explained above, and these may beused to filter the list further to generate a final list of users andassociated lookalike scores. If the request comprised a size of thefinal list desired by the requester (or if a size is predetermined ordetermined by other means), the list may be culled to generate a subsetlist of the users with the top lookalike scores of the size of the finallist desired.

The list may then be returned to the requester to be used by therequester, or the list may be used by the server system 102 to presentcontent from the requester. For example, the server system 102 mayreceive content from the requester (e.g., a media collection, an ad, amedia overlay, etc.) and the server system 102 may display the contentto one or more users of the plurality of users based on the generatedlist. For example, a user may be using a social networking or messagingsystem and the user may be associated with a unique identifier in thegenerated list. The server system 102 may display the content from therequester to the user during the user's use of the social networkingsystem or messaging system.

FIG. 4 is a block diagram illustrating another hybrid learning modelingsystem 400, according to some example embodiments. The system 400 shownin FIG. 4 combines lookalike results generated from a trained modelusing sample data and user profile features, with a social graphapproach. The idea is that if a user appears in both the lookalikeresults and social graph lookalike results, the user is a good candidatefor a final lookalike result. Thus, the user's lookalike score from thetrained model using sample data and user profile features is combinedwith the user's lookalike score from the social graph approach, and thenranking optimization is used to generate final lookalike results. In oneexample, the high-level steps include (1) computing each user'slookalike score (s_lal) using a trained model using sample data and userprofile features (e.g., logistic regression probability), (2) computingeach user's social graph lookalike model score (g_lal), for example,computed as a score for the seed data in a page rank method, furthermodifying the score by users' interaction with seeds (e.g., followee,messages sent, stories viewed, etc.), or another method, and (3)re-ranking users by combining s_lal and g_lal into h_lal, which is thefinal score.

To produce h_lal the server computer system solves for the parameters w1and w2, such that a predetermined business metric (e.g., retrospectiveoffline install rate or cost per install) is optimized:

argmax_w1_w2 predefined_objectives(users sort by (w1*s_lal+w2*g_lal))

This means that the final w1 and w2 will produce the largest predefinedobjective value. Typically this kind of ranking optimization does nothave close form solutions, so the w1 and w2 may be solved with thefollowing example approaches: (1) grid search in the space of w1 and w1,(2) simplex method or Nelder-Mead optimization in searching the space ofw1 and w2, (3) learning to rank method, which in particular solves foran optimal ranking with respect to objective functions (and this methodcan take w1 and w2 as two degrees of freedom), or (4) another approach.

The system 400 in FIG. 4 has several modules and databases that havebeen explained above with respect to system 200 in FIG. 2. Since thesame description from FIG. 2 applies to the similarly numbered modulesand data structures in FIG. 4, these similarly numbered items will notbe described again here.

FIG. 4 also comprises a social graph expansion module 410. The socialgraph expansion module 410 determines a predetermined feature orfeatures to use to compare users in user data for a plurality of usersto users in the seed data. Each user is scored based on how strong thefeature(s) for the user relates to the feature(s) for one or more seedusers. This score (e.g., g_lal) is stored in one or more databases 126D.

The ranking optimization module 416 provides functionality to merge thesocial graph LAL results (e.g., g_lal) and the LAL results (e.g., s_lal)in a way to maximize (or optimize) the number of users who will have themost desirable characteristics. The result is a final LAL score (h_lal)for each user which is stored in one or more databases 126H.

FIG. 5 is a flow chart illustrating aspects of a method 500, accordingto some example embodiments, for processing a LAL request. Forillustrative purposes, method 500 is described with respect to thenetworked system 100 of FIG. 1 and the block diagram of FIG. 4. It is tobe understood that method 500 may be practiced with other systemconfigurations in other embodiments.

In operation 502, a server computer system (e.g., server system 102 viaLAL modeling server and LAL request module 202), receives a request forlookalike data, as described above with respect to operation 302 of FIG.3. In operation 504, the server computer system generates sample data,as explained above with respect to operation 304 of FIG. 3. In operation506, the server computer system trains a lookalike model based on thesample data and, the user profile features for the plurality of users,to generate a trained lookalike model, as explained above with respectto operation 310 of FIG. 3. In operation 508, the server computer systemgenerates a lookalike score, for each user of the plurality of users,using the trained lookalike model, as explained above in respect tooperation 312 of FIG. 3.

In operation 510, the server computer system captures a snapshot of usersocial graph data, as explained above with respect to operation 306 ofFIG. 3. In operation 512 the server computer system uses the snapshot ofthe user social graph data to compute a social graph expansion. Forexample, the server computer system determines a predetermined featureor features to use to compare users in user data for a plurality ofusers to users in the seed data, using the snapshot of user social graphdata. A feature may be any sort of connection that can be establishedbetween two individuals or users. Some example features include, thenumber of times a user views another user's stories or mediacollections, whether or not a user collaborates with another user for astory or media collection, how many connections, such as friends, a userhas in common with a seed user, how many connections the seed users areaway from a particular user or other connections associated with theuser or seed users, how many times a user appears in the same locationas a seed user, whether the user is a follower of the seed user or theseed user's stories or media collections, how many times a userrepeatedly views a story or media collection of a seed user, whether theuser shared any media content with a seed user or vice versa, how long auser viewed a story or media collection of a seed user, what percent ofseed user stories or media collections a user viewed (e.g., 8 out of 10,2 out of 10, etc.), and the like.

The server computer system scores each user of the plurality of usersbased on how strong the feature(s) for each user relates to thefeature(s) for one or more seed users. For example, the server computersystem compares each user of the plurality of users to each user of theseed users to determine a value for each feature based on the featureand connectivity related to the users and feature (e.g., a score of therelationships of each user of the plurality of users compared to eachuser of the seed users). For example, user A of the plurality of usersis compared to user B of the seed users to determine whether they are ineach other's social graph and how many connections they are away in thesocial graphs. This score (e.g., g_lal) is stored in one or moredatabases 126D.

Using a simple example of connections between the users of the pluralityof users and the seed users, there may be a seed user A and a seed userB in the seed users. There may be users 1-10 in the plurality of users.Seed user A may have connections with users 1 and 2, and seed user B mayhave connections with users 1, 2, and 7. In this example, user 1 gets ascore of 2 because user 1 has connections with 2 seed users (e.g., seeduser A and seed user B), user 2 gets a score of 2 because user 2 hasconnections with 2 seed users (e.g., seed user A and seed user B), anduser 7 gets a score of 1 because user 7 has a connection with 1 seeduser (e.g., seed user B), and the rest of the users 3-6 and 8-10 get ascore of 0 because they have no connections with any seed users. Thescore may further reflect how far away the connections are (e.g., directconnection, two friends away, ten friends away, etc.) and othercriteria. As explained above, there are various other features that maybe used, including how often the user chats or sends messages with oneor more seed users, how many seed users the user collaborates with on astory or media collection, how many seed user stories or mediacollections has the user viewed, and so forth. All of the predeterminedfeatures may be used to determine the final social graph score for eachuser of the plurality of users (e.g., social graph LAL results).

In operation 514, the server computer system analyzes the LAL results(e.g., the s_lal scores) and the social graph LAL results (e.g., theg_lal scores) to perform optimized ranking of the users of the pluralityof users based on the generated lookalike scores for each user and thesocial graph expansion scores for each user. For example, the servercomputer system merges the s_lal scores and the g_lal scores to combinethe scores in such a way to maximize the number of users who willcomprise the desired characteristics. In one example, the servercomputer system may use a weighted sum to perform this calculation.

In operation 516, the server computer system generates a final lookalikescore for each user of the plurality of users based on the optimizedranking. As explained above, after generating the list of users andassociated lookalike scores, the list may be further culled based onexclusions or constraints from the entity associated with the request.For example, the request may also comprise exclusions, constraints, asexplained above, and these may be used to filter the list further togenerate a final list of users and associated lookalike scores. If therequest comprised a size of the final list desired by the requester (orif a size is predetermined or determined by other means), the list maybe culled to generate a subset list of the users with the top lookalikescores of the size of the final list desired.

The list may then be returned to the requester to be used by therequester, or the list may be used by the server system 102 to presentcontent from the requester. For example, the server system 102 mayreceive content from the requester (e.g., a media collection, an ad, amedia overlay, etc.) and the server system 102 may display the contentto one or more users of the plurality of users based on the generatedlist. For example, a user may be using a social networking or messagingsystem and the user may be associated with a unique identifier in thegenerated list. The server system 102 may display the content from therequester to the user during the user's use of the social networkingsystem or messaging system.

In another example embodiment, another hybrid approach integrating bothLAL results (e.g., capturing similarity in user content engagement) andsocial graph data (e.g., similarity from influence and homophily) may beused. This approach may be built on top of state-of-the-art labelpropagation.

Label propagation, in general, tries to label the data points near thelabeled points with similar labels. A label propagation in a socialgraph lookalike tries to label users which can be traced back to seedsthrough a social graph as “seeds,” and hence can include those usersinto a lookalike segment. But since we already have supervised learninglookalike segment and the lookalike score s_lal (also referred to as a“supervised learning lookalike score”), the lookalike score may beincorporated as a degree of stochasticness when propagating the label.Example pseudocode for this example embodiment comprises:

-   total_two_hop_score=0-   propogation_probabilty=0-   for each user ui:    -   h_lal(ui)=0 # initialize ui's final score to 0    -   for each seed si:        -   two_hop_score=        -   (# two hop paths from si to ui in friends graph)/# friends            of si total_two_hop_score=total_two_hop_score+two_hop_score            propogation_probability=s_lal(ui) # ui's supervised learning            lookalike score val=uniform(0,1) # generate a random            variable from uniform distribution from 0 to 1    -   if val<=propogation_probability:    -   h_lal(ui)=total_two_hop_score # allow propagation-   sort all users based on h_lal(user)-   take top X as hybrid lookalike segment

As shown in the above example pseudocode, the stochastic labelpropagation approach can be viewed as a probabilistic version rank.

FIG. 6 is a flow chart illustrating aspects of a method 600, accordingto some example embodiments, for processing a LAL request. Forillustrative purposes, method 600 is described with respect to thenetworked system 100 of FIG. 1 and the block diagram of FIG. 4. It is tobe understood that method 600 may be practiced with other systemconfigurations in other embodiments.

Operations 602-612 are the same as the operations 502-512 of FIG. 5 andare explained above with respect to operations 502-512. In operation614, the server computer system performs a probability calculation onsocial graph expansion scores (e.g., social graph LAL results (g_lal)stored in one or more databases 126D) using lookalike scores (e.g., LALresults (s_lal) stored in one or more databases 126E). For example, foreach user in the social graph LAL results, the server computer systemuses the computed lookalike score (e.g., a number between 0-1) todetermine the probability that the user in the social graph lookalikeexpansion will be included or eliminated from the final LAL results. Inone example, the server computer system uses a uniform number generatorto randomly generate a number to indicate whether or not the user shouldbe included or eliminated from the final LAL results.

For example, a user A may have a social graph score (g_lal) of 500 and alookalike score (s_lal) of 0.1, a user B may have a social graph scoreof 300 and a lookalike score 0.5, and a user C may have a social graphscore of 200 and a lookalike score of 0.4. Based on these scores, user Ahas a 0.1 (e.g., 10%) probability of being included in the final LALresults, user B has a 0.5 (e.g., 50%) probability of being included inthe final LAL results, and user C has a 0.4 (e.g., 40%) probability ofbeing included in the final LAL results.

Based on the results of the probability calculation, the server computersystem determines whether or not the user is included or eliminated. Ifthe user is included, the user's social graph score (e.g., g_lal) is thescore calculated based on the social graph snapshot data. If the user isto be eliminated, the user's social graph score is set to zero or theuser is removed from the list of users.

In operation 616, the server computer system generates a final lookalikescore for each user of the plurality of users based on the social graphLAL results (e.g., g_lal) and the LAL results (s_lal). For example, theserver computer system may generate the final lookalike score for eachuser by performing optimized ranking as explained above or by othermethods for combining or merging the scores.

As explained above, after the list of users and associated lookalikescores is generated, the list may be further culled based on exclusionsor constraints from the entity associated with the request. For example,the request may also comprise exclusions, constraints, as explainedabove, and these may be used to filter the list further to generate afinal list of users and associated lookalike scores. If the requestcomprised a size of the final list desired by the requester (or if asize is predetermined or determined by other means), the list may beculled to generate a subset list of the users with the top lookalikescores of the size of the final list desired.

The list may then be returned to the requester to be used by therequester, or the list may be used by the server system 102 to presentcontent from the requester. For example, the server system 102 mayreceive content from the requester (e.g., a media collection, an ad, amedia overlay, etc.) and the server system 102 may display the contentto one or more users of the plurality of users based on the generatedlist. For example, a user may be using a social networking or messagingsystem and the user may be associated with a unique identifier in thegenerated list. The server system 102 may display the content from therequester to the user during the user's use of the social networkingsystem or messaging system.

The above examples describe ranking users by lookalike scores. In otherexamples, content may be ranked for each user. For example, a socialnetworking system or messaging system may provide various channels ofcontent (e.g., 10 channels, 26 channels, etc.). For each channel alookalike model could be built and then run for each user. The channelswould be ranked in order for each user to determine which content shouldappear first to the user (e.g., first in a plurality of content in asocial networking or messaging system user interface on a computingdevice).

In one example, the lookalike model is built for each channel based onall users in a particular time period (e.g., one week, one month, oneyear) that viewed that channel at a particular frequency (e.g., everyday, every other day, every week, etc.). The model may further be builtbased on a duration of time that the user viewed the channel (e.g., formore than a predetermined amount of time such as a minute, five minutes,ten minutes, etc.).

For example, there may be a Cosmopolitan channel and a CNN channel. Alookalike model may be built for the Cosmopolitan channel based on allthe users in the last six months that viewed the Cosmopolitan channel atleast once every day. Similarly, a lookalike model may be built for theCNN channel. The lookalike model for each channel is used to score allof the other users in the system (or a set of users for which a score isdesired). Thus, the system would generate a lookalike score for eachuser for each channel for how likely they are to view each channel. Forexample, a user may have a 0.1 score for CNN and a 0.5 score forCosmopolitan. The system can rank the scores for each user to generatean order to display the channels on a user computing device. In theexample above, the CNN channel would appear first and the Cosmopolitanchannel would appear second for the user.

As explained above, the server system 102 of FIG. 1 may be associatedwith a messaging system or social networking system. For example, theserver system 102 may be a messaging system or social networking system,incorporated into a messaging system or social networking system, or incommunication with a messaging system or social networking system.

FIG. 7 is a block diagram illustrating a networked system 700 (e.g., amessaging system) for exchanging data (e.g., messages and associatedcontent) over a network. The networked system 700 includes multipleclient devices 110, each of which hosts a number of client applications114. Each client application 114 is communicatively coupled to otherinstances of the client application 114 and a server system 708 via anetwork 104.

The client device 110, client application 114, and network 104, aredescribed above with respect to FIG. 1. The client device 110 may be adevice of a user that is used to create media content items such asvideo, images (e.g., photographs), audio, and send and receive messagescontaining such media content items to and from other users.

In one example, a client application 114 may be a messaging applicationthat allows a user to take a photograph or video, add a caption, orotherwise edit the photograph or video, and then send the photograph orvideo to another user. The message may be ephemeral and be removed froma receiving user device after viewing or after a predetermined amount oftime (e.g., 10 seconds, 24 hours, etc.). An ephemeral message refers toa message that is accessible for a time-limited duration. An ephemeralmessage may be a text, an image, a video and other such content that maybe stitched together in accordance with embodiments described herein.The access time for the ephemeral message may be set by the messagesender. Alternatively, the access time may be a default setting or asetting specified by the recipient. Regardless of the setting technique,the message is transitory.

The messaging application may further allow a user to create a galleryor message collection. A gallery may be a collection of photos andvideos which may be viewed by other users “following” the user's gallery(e.g., subscribed to view and receive updates in the user's gallery).The gallery may also be ephemeral (e.g., lasting 24 hours, lasting for aduration of an event (e.g., during a music concert, sporting event,etc.), or other predetermined time).

An ephemeral message may be associated with a message durationparameter, the value of which determines an amount of time that theephemeral message will be displayed to a receiving user of the ephemeralmessage by the client application 114. The ephemeral message may befurther associated with a message receiver identifier and a messagetimer. The message timer may be responsible for determining the amountof time the ephemeral message is shown to a particular receiving useridentified by the message receiver identifier. For example, theephemeral message may only be shown to the relevant receiving user for atime period determined by the value of the message duration parameter.

In another example, the messaging application may allow a user to storephotographs and videos and create a gallery that is not ephemeral andthat can be sent to other users; for example, to assemble photographsand videos from a recent vacation to share with friends and family.

A server system 708 may provide server-side functionality via thenetwork 104 (e.g., the Internet or wide area network (WAN)) to one ormore client device 110. The server system 708 may include an applicationprogramming interface (API) server 710, an application server 712, amessaging application server 714, a media content processing system 716,and a social network system 722, which may each be communicativelycoupled with each other and with one or more data storage(s), such asdatabase(s) 720. The server system 708 may also comprise the serversystem 102 of FIG. 1 or at least the LAL modeling server 124 of FIG. 1.

The server system 708 may be a cloud computing environment, according tosome example embodiments. The server system 708, and any serversassociated with the server system 708, may be associated with acloud-based application, in one example embodiment. The one or moredatabase(s) 720 may be storage devices that store information such asuntreated media content, original media content from users (e.g.,high-quality media content), processed media content (e.g., mediacontent that is formatted for sharing with client devices 110 andviewing on client devices 110), context data related to a media contentitem, user information, user device information, LAL data, and so forth.The one or more database(s) 720 may include cloud-based storage externalto the server system 708 (e.g., hosted by one or more third partyentities external to the server system 708). While the storage devicesare shown as database(s) 720, it is understood that the system 100 mayaccess and store data in storage devices such as databases 720, blobstorages, and other type of storage methods.

Accordingly, each client application 114 is able to communicate andexchange data with other client applications 114 and with the serversystem 708 via the network 104. The data exchanged between clientapplications 114, and between a client application 114 and the serversystem 708, includes functions (e.g., commands to invoke functions) aswell as payload data (e.g., text, audio, video or other multimediadata).

The server system 708 provides server-side functionality via the network104 to a particular client application 114. While certain functions ofthe system 700 are described herein as being performed by either aclient application 114 or by the server system 708, it will beappreciated that the location of certain functionality either within theclient application 114 or the server system 708 is a design choice. Forexample, it may be technically preferable to initially deploy certaintechnology and functionality within the server system 708, but to latermigrate this technology and functionality to the client application 114where a client device 110 has a sufficient processing capacity.

The server system 708 supports various services and operations that areprovided to the client application 114. Such operations includetransmitting data to, receiving data from, and processing data generatedby the client application 114. This data may include message content,client device information, geolocation information, media annotation andoverlays, message content persistence conditions, social networkinformation, live event information, date and time stamps, as examples.Data exchanges within the networked system 700 are invoked andcontrolled through functions available via user interfaces (UIs) of theclient application 114.

In the server system 708, an application program interface (API) server710 is coupled to, and provides a programmatic interface to, anapplication server 712. The application server 712 is communicativelycoupled to a database server 718, which facilitates access to one ormore database(s) 720 in which is stored data associated with messagesprocessed by the application server 712.

The API server 710 receives and transmits message data (e.g., commandsand message payloads) between the client device 110 and the applicationserver 712. Specifically, the API server 710 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the client application 114 in order to invoke functionality of theapplication server 712. The API server 710 exposes various functionssupported by the application server 712, including account registration;login functionality; the sending of messages, via the application server712, from a particular client application 114 to another clientapplication 114; the sending of media files (e.g., images or video) froma client application 114 to the messaging application server 714, andfor possible access by another client application 114; the setting of acollection of media data (e.g., a story); the retrieval of a list offriends of a user of a client device 110; the retrieval of suchcollections; the retrieval of messages and content; the adding anddeletion of friends to a social graph, the location of friends within asocial graph; opening an application event (e.g., relating to the clientapplication 114), and so forth.

The application server 712 hosts a number of applications andsubsystems, including a messaging application server 714, a mediacontent processing system 716, and a social network system 722. Themessaging application server 714 implements a number of messageprocessing technologies and functions, particularly related to theaggregation and other processing of content (e.g., textual andmultimedia content) included in messages received from multipleinstances of the messaging client application 114. As will be describedin further detail, the text and media content from multiple sources maybe aggregated into collections of content (e.g., called stories orgalleries). These collections are then made available, by the messagingapplication server 714, to the client application 114. Other processor-and memory-intensive processing of data may also be performedserver-side by the messaging application server 714, in view of thehardware requirements for such processing.

The application server 712 also includes a media content processingsystem 716 that is dedicated to performing various media contentprocessing operations, typically with respect to images or videoreceived within the payload of a message at the messaging applicationserver 714. The media content processing system 716 may access one ormore data storages (e.g., database(s) 720) to retrieve stored data touse in processing media content and to store results of processed mediacontent.

The social network system 722 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging application server 714. To this end, the social networksystem 722 maintains and accesses an entity graph 904 (depicted in FIG.9) within the database 720. Examples of functions and services supportedby the social network system 722 include the identification of otherusers of the networked system 700 with which a particular user hasrelationships or is “following,” and also the identification of otherentities and interests of a particular user.

The messaging application server 714 may be responsible for generationand delivery of messages between users of client devices 110. Themessaging application server 714 may utilize any one of a number ofmessage delivery networks and platforms to deliver messages to users.For example, the messaging application server 714 may deliver messagesusing electronic mail (e-mail), instant message (IM), Short MessageService (SMS), text, facsimile, or voice (e.g., Voice over IP (VoIP))messages via wired networks (e.g., the Internet), plain old telephoneservice (POTS), or wireless networks (e.g., mobile, cellular, WiFi, LongTerm Evolution (LTE), Bluetooth).

FIG. 8 is block diagram 800 illustrating further details regarding thesystem 700, according to example embodiments. Specifically, the system700 is shown to comprise the messaging client application 114 and theapplication server 712, which in turn embody a number of subsystems,namely an ephemeral timer system 802, a collection management system804, and an annotation system 806.

The ephemeral timer system 802 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 114 and the messaging application server 714. To this end,the ephemeral timer system 802 incorporates a number of timers that,based on duration and display parameters associated with a message, orcollection of messages (e.g., a SNAP or SNAPCHAT Story), selectivelydisplay and enable access to messages and associated content via themessaging client application 114.

The collection management system 804 is responsible for managingcollections of media (e.g., collections of text, image video and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text and audio) may be organized into an “eventgallery” or an “event story.” Such a collection may be made availablefor a specified time period, such as the duration of an event to whichthe content relates. For example, content relating to a music concertmay be made available as a “Story” for the duration of that musicconcert. The collection management system 804 may also be responsiblefor publishing an icon that provides notification of the existence of aparticular collection to the user interface of the messaging clientapplication 114.

The collection management system 804 furthermore includes a curationinterface 808 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface808 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 804employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation (e.g., money, non-money credits or pointsassociated with the communication system or a third party reward system,travel miles, access to artwork or specialized lenses, etcetera) may bepaid to a user for inclusion of user-generated content into acollection. In such cases, the curation interface 808 operates toautomatically make payments to such users for the use of their content.

The annotation system 806 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 806 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the networked system 700. The annotation system 806operatively supplies a media overlay (e.g., a SNAPCHAT filter) to themessaging client application 114 based on a geolocation of the clientdevice 110. In another example, the annotation system 806 operativelysupplies a media overlay to the messaging client application 114 basedon other information, such as social network information of the user ofthe client device 110. A media overlay may include audio and visualcontent and visual effects. Examples of audio and visual content includepictures, texts, logos, animations, and sound effects. An example of avisual effect includes color overlaying. The audio and visual content orthe visual effects can be applied to a media content item (e.g., aphoto) at the client device 110. For example, the media overlayincluding text that can be overlaid on top of a photograph taken by theclient device 110. In another example, the media overlay includes anidentification of a location overlay (e.g., Venice beach), a name of alive event, or a name of a merchant overlay (e.g., Beach Coffee House).In another example, the annotation system 806 uses the geolocation ofthe client device 110 to identify a media overlay that includes the nameof a merchant at the geolocation of the client device 110. The mediaoverlay may include other indicia associated with the merchant. Themedia overlays may be stored in the database 720 and accessed throughthe database server 718.

In one example embodiment, the annotation system 806 provides auser-based publication platform that enables users to select ageolocation on a map, and upload content associated with the selectedgeolocation. The user may also specify circumstances under which aparticular media overlay is to be offered to other users. The annotationsystem 806 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 806 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 806 associates the mediaoverlay of a highest bidding merchant with a corresponding geolocationfor a predefined amount of time

FIG. 9 is a schematic diagram 900 illustrating data which may be storedin the database(s) 720 of the server system 708, according to certainexample embodiments. While the content of the database 720 is shown tocomprise a number of tables, it will be appreciated that the data couldbe stored in other types of data structures (e.g., as an object-orienteddatabase).

The database 720 includes message data stored within a message table914. The entity table 902 stores entity data, including an entity graph904. Entities for which records are maintained within the entity table902 may include individuals, corporate entities, organizations, objects,places, events, etc. Regardless of type, any entity regarding which theserver system 708 stores data may be a recognized entity. Each entity isprovided with a unique identifier, as well as an entity type identifier(not shown).

The entity graph 904 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization) interested-based or activity-based, merely for example.

The database 720 also stores annotation data, in the example form offilters, in an annotation table 912. Annotation data may also bereferred to herein as “creative tools.” Filters for which data is storedwithin the annotation table 912 are associated with and applied tovideos (for which data is stored in a video table 910) and/or images(for which data is stored in an image table 908). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 114 whenthe sending user is composing a message. Other types of filers includegeolocation filters (also known as geo-filters) which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 114, basedon geolocation information determined by a GPS unit of the client device110. Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client application 114,based on other inputs or information gathered by the client device 110during the message creation process. Example of data filters includecurrent temperature at a specific location, a current speed at which asending user is traveling, battery life for a client device 110, or thecurrent time.

Other annotation data that may be stored within the image table 908 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 910 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 914. Similarly, the image table 908 storesimage data associated with messages for which message data is stored inthe entity table 902. The entity table 902 may associate variousannotations from the annotation table 912 with various images and videosstored in the image table 908 and the video table 910.

A story table 906 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a SNAPCHAT story or a gallery). The creation of aparticular collection may be initiated by a particular user (e.g., eachuser for which a record is maintained in the entity table 902). A usermay create a “personal story” in the form of a collection of contentthat has been created and sent/broadcast by that user. To this end, theuser interface of the messaging client application 114 may include anicon that is user-selectable to enable a sending user to add specificcontent to his or her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users, whose client devices 110 havelocation services enabled and are at a common location event at aparticular time, may, for example, be presented with an option, via auser interface of the messaging client application 114, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 114, based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story”,which enables a user whose client device 110 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 10 is a schematic diagram illustrating a structure of a message1000, according to some in some embodiments, generated by a clientapplication 114 for communication to a further client application 114 orthe messaging application server 714. The content of a particularmessage 1000 is used to populate the message table 914 stored within thedatabase 720, accessible by the messaging application server 714.Similarly, the content of a message 1000 is stored in memory as“in-transit” or “in-flight” data of the client device 110 or theapplication server 712. The message 1000 is shown to include thefollowing components:

-   -   A message identifier 1002: a unique identifier that identifies        the message 1000.    -   A message text payload 1004: text, to be generated by a user via        a user interface of the client device 110 and that is included        in the message 1000.    -   A message image payload 1006: image data, captured by a camera        component of a client device 110 or retrieved from memory of a        client device 110, and that is included in the message 1000.    -   A message video payload 1008: video data, captured by a camera        component or retrieved from a memory component of the client        device 110 and that is included in the message 1000.    -   A message audio payload 1010: audio data, captured by a        microphone or retrieved from the memory component of the client        device 110, and that is included in the message 1000.    -   A message annotations 1012: annotation data (e.g., filters,        stickers or other enhancements) that represents annotations to        be applied to message image payload 1006, message video payload        1008, or message audio payload 1010 of the message 1000.    -   A message duration parameter 1014: parameter value indicating,        in seconds, the amount of time for which content of the message        1000 (e.g., the message image payload 1006, message video        payload 1008, message audio payload 1010) is to be presented or        made accessible to a user via the messaging client application        114.    -   A message geolocation parameter 1016: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 1000. Multiple message        geolocation parameter 1016 values may be included in the        payload, each of these parameter values being associated with        respect to content items included in the content (e.g., a        specific image within the message image payload 1006, or a        specific video in the message video payload 1008).    -   A message story identifier 1018: identifier values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 1006 of the        message 1000 is associated. For example, multiple images within        the message image payload 1006 may each be associated with        multiple content collections using identifier values.    -   A message tag 1020: each message 1000 may be tagged with        multiple tags, each of which is indicative of the subject matter        of content included in the message payload. For example, where a        particular image included in the message image payload 1006        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 1020 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   A message sender identifier 1022: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 110 on        which the message 1000 was generated and from which the message        1000 was sent.    -   A message receiver identifier 1024: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 110 to        which the message 1000 is addressed.

The contents (e.g., values) of the various components of message 1000may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload1006 may be a pointer to (or address of) a location within an imagetable 908. Similarly, values within the message video payload 1008 maypoint to data stored within a video table 910, values stored within themessage annotations 1012 may point to data stored in an annotation table912, values stored within the message story identifier 1018 may point todata stored in a story table 906, and values stored within the messagesender identifier 1022 and the message receiver identifier 1024 maypoint to user records stored within an entity table 902.

The following examples describe various embodiments of methods,machine-readable media, and systems (e.g., machines, devices, or otherapparatus) discussed herein.

-   Example 1. A method comprising:    -   receiving, at a server computer system, a request for lookalike        data, the request for lookalike data comprising seed data;    -   generating, by the server computer system, sample data from the        seed data and from user data for a plurality of users, to use in        a lookalike model training;    -   capturing, by the server computer system, a snapshot of social        graph data for a plurality of users and computing social graph        features based on the seed data and the user data for the        plurality of users;    -   training, by the server computer system, a lookalike model based        on the sample data, user profile features for the plurality of        users, and the computed social graph features to generate a        trained lookalike model;    -   generating, by the server computer system, a lookalike score for        each user of the plurality of users in the user data using the        trained lookalike model; and    -   generating, by the server computer system, a list comprising a        unique identifier for each user of the plurality of users and an        associated lookalike score for each unique identifier.-   Example 2. A method according to Example 1, wherein generating the    sample data, from the seed data and from the user data for the    plurality of users, to use in the lookalike model training,    comprises:    -   generating a positive data sample from the seed data to use in        the lookalike model training; and    -   generating a negative data sample from user data stored in a        database to use in the lookalike model training;-   Example 3. A method according to any of the previous examples,    wherein the positive data sample comprises the seed data.-   Example 4. A method according to any of the previous examples,    wherein the negative data sample comprises a subset of the user data    for the plurality of users.-   Example 5. A method according to any of the previous examples,    further comprising:    -   capturing a user profile snapshot;    -   generating user profile feature data; and    -   storing the user profile feature data.-   Example 6. A method according to any of the previous examples,    wherein the request further comprises filter characteristics, and    the method further comprises:    -   filtering the list comprising each user and an associated        lookalike score for each user, based on the filter        characteristics received in the request; and    -   associating the filtered list with the request.-   Example 7. A method according to any of the previous examples,    wherein the seed data comprises a plurality of user identifiers.-   Example 8. A method according to any of the previous examples,    wherein the generated list comprising a unique identifier for each    user of the plurality of users, and an associated lookalike score    for each unique identifier, is of a size indicated by the request    for the lookalike data.-   Example 9. A method according to any of the previous examples,    wherein the plurality of users are users of a messaging system or    social networking system.-   Example 10. A method according to any of the previous examples,    wherein the generated list is associated with a requester that sent    the request for the lookalike data, and the method further    comprises:    -   receiving content from the requester; and    -   displaying the content to one or more users of the plurality of        users, based on the generated list.-   Example 11. A server computer comprising:    -   one or more hardware processors; and    -   a computer-readable medium coupled with the one or more hardware        processors, the computer-readable medium comprising instructions        stored thereon that are executable by the one or more hardware        processors to cause the server computer to perform operations        comprising:        -   receiving a request for lookalike data, the request for            lookalike data comprising seed data;        -   generating sample data from the seed data and from user data            for a plurality of users, to use in a lookalike model            training;        -   capturing a snapshot of social graph data for a plurality of            users and computing social graph features based on the seed            data and the user data for the plurality of users;        -   training a lookalike model based on the sample data, user            profile features for the plurality of users, and the            computed social graph features to generate a trained            lookalike model;        -   generating a lookalike score for each user of the plurality            of users in the user data using the trained lookalike model;            and        -   generating a list comprising a unique identifier for each            user of the plurality of users and an associated lookalike            score for each unique identifier.-   Example 12. A server computer according to any of the previous    examples, wherein generating the sample data, from the seed data and    from the user data for the plurality of users, to use in the    lookalike model training, comprises:    -   generating a positive data sample from the seed data to use in        the lookalike model training; and    -   generating a negative data sample from user data stored in a        database to use in the lookalike model training.-   Example 13. A server computer according to any of the previous    examples, wherein the positive data sample comprises the seed data.-   Example 14. A server computer according to any of the previous    examples, wherein the negative data sample comprises a subset of the    user data for the plurality of users.-   Example 15. A server computer according to any of the previous    examples, the operations further comprising:    -   capturing a user profile snapshot;    -   generating user profile feature data; and    -   storing the user profile feature data.-   Example 16. A server computer according to any of the previous    examples, wherein the request further comprises filter    characteristics, and the operations further comprise:    -   filtering the list comprising each user and an associated        lookalike score for each user, based on the filter        characteristics received in the request; and    -   associating the filtered list with the request.-   Example 17. A server computer according to any of the previous    examples, wherein the generated list comprising a unique identifier    for each user of the plurality of users, and an associated lookalike    score for each unique identifier, is of a size indicated by the    request for the lookalike data.-   Example 18. A server computer according to any of the previous    examples, wherein the plurality of users are users of a messaging    system or social networking system.-   Example 19. A server computer according to any of the previous    examples, wherein the generated list is associated with a requester    that sent the request for the lookalike data, and the operations    further comprise:    -   receiving content from the requester; and    -   displaying the content to one or more users of the plurality of        users, based on the generated list.-   Example 20. A non-transitory computer-readable medium comprising    instructions stored thereon that are executable by at least one    processor to cause a computing device to perform operations    comprising:    -   receiving a request for lookalike data, the request for        lookalike data comprising seed data;    -   generating sample data from the seed data and from user data for        a plurality of users, to use in a lookalike model training;    -   capturing a snapshot of social graph data for a plurality of        users and computing social graph features based on the seed data        and the user data for the plurality of users;    -   training a lookalike model based on the sample data, user        profile features for the plurality of users, and the computed        social graph features to generate a trained lookalike model;    -   generating a lookalike score for each user of the plurality of        users in the user data using the trained lookalike model; and        generating a list comprising a unique identifier for each user        of the plurality of users and an associated lookalike score for        each unique identifier.

FIG. 11 is a block diagram 1100 illustrating software architecture 1102,which can be installed on any one or more of the devices describedabove. For example, in various embodiments, client devices 110 andserver systems 102, 120, 122, 124, 130, 708, 710, 712, 714, 716, 722 maybe implemented using some or all of the elements of softwarearchitecture 1102. FIG. 11 is merely a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures can be implemented to facilitate the functionalitydescribed herein. In various embodiments, the software architecture 1102is implemented by hardware such as machine 1200 of FIG. 12 that includesprocessors 1210, memory 1230, and I/O components 1250. In this example,the software architecture 1102 can be conceptualized as a stack oflayers where each layer may provide a particular functionality. Forexample, the software architecture 1102 includes layers such as anoperating system 1104, libraries 1106, frameworks 1108, and applications1110. Operationally, the applications 1110 invoke API calls 1112 throughthe software stack and receive messages 1114 in response to the APIcalls 1112, consistent with some embodiments.

In various implementations, the operating system 1104 manages hardwareresources and provides common services. The operating system 1104includes, for example, a kernel 1120, services 1122, and drivers 1124.The kernel 1120 acts as an abstraction layer between the hardware andthe other software layers, consistent with some embodiments. Forexample, the kernel 1120 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1122 canprovide other common services for the other software layers. The drivers1124 are responsible for controlling or interfacing with the underlyinghardware, according to some embodiments. For instance, the drivers 1124can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH®Low Energy drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audiodrivers, power management drivers, and so forth.

In some embodiments, the libraries 1106 provide a low-level commoninfrastructure utilized by the applications 1110. The libraries 1106 caninclude system libraries 1130 (e.g., C standard library) that canprovide functions such as memory allocation functions, stringmanipulation functions, mathematic functions, and the like. In addition,the libraries 1106 can include API libraries 1132 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as Moving Picture Experts Group-4 (MPEG4),Advanced Video Coding (H.264 or AVC), Moving Picture Experts GroupLayer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR)audio codec, Joint Photographic Experts Group (JPEG or JPG), or PortableNetwork Graphics (PNG)), graphics libraries (e.g., an OpenGL frameworkused to render in two dimensions (2D) and in three dimensions (3D)graphic content on a display), database libraries (e.g., SQLite toprovide various relational database functions), web libraries (e.g.,WebKit to provide web browsing functionality), and the like. Thelibraries 1106 can also include a wide variety of other libraries 1134to provide many other APIs to the applications 1110.

The frameworks 1108 provide a high-level common infrastructure that canbe utilized by the applications 1110, according to some embodiments. Forexample, the frameworks 1108 provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks 1108 can provide a broad spectrumof other APIs that can be utilized by the applications 1110, some ofwhich may be specific to a particular operating system 1104 or platform.

In an example embodiment, the applications 1110 include a homeapplication 1150, a contacts application 1152, a browser application1154, a book reader application 1156, a location application 1158, amedia application 1160, a messaging application 1162, a game application1164, and a broad assortment of other applications such as a third partyapplications 1166. According to some embodiments, the applications 1110are programs that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 1110, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third party application 1166 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third party application 1166 can invoke the API calls 1112provided by the operating system 1104 to facilitate functionalitydescribed herein.

Some embodiments may particularly include a social networkingapplication 1167. In certain embodiments, this may be a stand-aloneapplication that operates to manage communications with a server systemsuch as third party servers 130 or server system 102 or 708. In otherembodiments, this functionality may be integrated with anotherapplication. The social networking application 1167 may request anddisplay various data related to messaging, media content, mediacollections, and so forth, and may provide the capability for a user 106to input data related to the system via a touch interface, keyboard, orusing a camera device of machine 1200, communication with a serversystem via I/O components 1250, and receipt and storage of object datain memory 1230. Presentation of information and user inputs associatedwith the information may be managed by social networking application1167 using different frameworks 1108, library 1106 elements, oroperating system 1104 elements operating on a machine 1200.

FIG. 12 is a block diagram illustrating components of a machine 1200,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 12 shows a diagrammatic representation of the machine1200 in the example form of a computer system, within which instructions1216 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1200 to perform any oneor more of the methodologies discussed herein can be executed. Inalternative embodiments, the machine 1200 operates as a standalonedevice or can be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1200 may operate in the capacity of aserver machine 102, 120, 122, 124, 130, 708, 710, 712, 714, 716, 722,and the like, or a client device 110 in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1200 can comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a personal digitalassistant (PDA), an entertainment media system, a cellular telephone, asmart phone, a mobile device, a wearable device (e.g., a smart watch), asmart home device (e.g., a smart appliance), other smart devices, a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 1216, sequentially orotherwise, that specify actions to be taken by the machine 1200.Further, while only a single machine 1200 is illustrated, the term“machine” shall also be taken to include a collection of machines 1200that individually or jointly execute the instructions 1216 to performany one or more of the methodologies discussed herein.

In various embodiments, the machine 1200 comprises processors 1210,memory 1230, and I/O components 1250, which can be configured tocommunicate with each other via a bus 1202. In an example embodiment,the processors 1210 (e.g., a central processing unit (CPU), a reducedinstruction set computing (RISC) processor, a complex instruction setcomputing (CISC) processor, a graphics processing unit (GPU), a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a radio-frequency integrated circuit (RFIC), another processor,or any suitable combination thereof) include, for example, a processor1212 and a processor 1214 that may execute the instructions 1216. Theterm “processor” is intended to include multi-core processors 1210 thatmay comprise two or more independent processors 1212, 1214 (alsoreferred to as “cores”) that can execute instructions 1216contemporaneously. Although FIG. 12 shows multiple processors 1210, themachine 1200 may include a single processor 1210 with a single core, asingle processor 1210 with multiple cores (e.g., a multi-core processor1210), multiple processors 1212, 1214 with a single core, multipleprocessors 1212, 1214 with multiples cores, or any combination thereof.

The memory 1230 comprises a main memory 1232, a static memory 1234, anda storage unit 1236 accessible to the processors 1210 via the bus 1202,according to some embodiments. The storage unit 1236 can include amachine-readable medium 1238 on which are stored the instructions 1216embodying any one or more of the methodologies or functions describedherein. The instructions 1216 can also reside, completely or at leastpartially, within the main memory 1232, within the static memory 1234,within at least one of the processors 1210 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1200. Accordingly, in various embodiments, themain memory 1232, the static memory 1234, and the processors 1210 areconsidered machine-readable media 1238.

As used herein, the term “memory” refers to a machine-readable medium1238 able to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1238 is shown, in an example embodiment, to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 1216. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 1216) for executionby a machine (e.g., machine 1200), such that the instructions 1216, whenexecuted by one or more processors of the machine 1200 (e.g., processors1210), cause the machine 1200 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,erasable programmable read-only memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 1250 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 1250 can include many othercomponents that are not shown in FIG. 12. The I/O components 1250 aregrouped according to functionality merely for simplifying the followingdiscussion, and the grouping is in no way limiting. In various exampleembodiments, the I/O components 1250 include output components 1252 andinput components 1254. The output components 1252 include visualcomponents (e.g., a display such as a plasma display panel (PDP), alight emitting diode (LED) display, a liquid crystal display (LCD), aprojector, or a cathode ray tube (CRT)), acoustic components (e.g.,speakers), haptic components (e.g., a vibratory motor), other signalgenerators, and so forth. The input components 1254 include alphanumericinput components (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In some further example embodiments, the I/O components 1250 includebiometric components 1256, motion components 1258, environmentalcomponents 1260, or position components 1262, among a wide array ofother components. For example, the biometric components 1256 includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1258 includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1260 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensor components(e.g., machine olfaction detection sensors, gas detection sensors todetect concentrations of hazardous gases for safety or to measurepollutants in the atmosphere), or other components that may provideindications, measurements, or signals corresponding to a surroundingphysical environment. The position components 1262 include locationsensor components (e.g., a Global Positioning System (GPS) receivercomponent), altitude sensor components (e.g., altimeters or barometersthat detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 1250 may include communication components 1264operable to couple the machine 1200 to a network 1280 or devices 1270via a coupling 1282 and a coupling 1272, respectively. For example, thecommunication components 1264 include a network interface component oranother suitable device to interface with the network 1280. In furtherexamples, communication components 1264 include wired communicationcomponents, wireless communication components, cellular communicationcomponents, near field communication (NFC) components, BLUETOOTH®components (e.g., BLUETOOTH® Low Energy), WI-FI® components, and othercommunication components to provide communication via other modalities.The devices 1270 may be another machine 1200 or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, in some embodiments, the communication components 1264 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 1264 include radio frequencyidentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detecta one-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code,Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 1264, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting a BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 1280can be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the publicswitched telephone network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a WI-FI®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1280 or a portion of the network 1280may include a wireless or cellular network, and the coupling 1282 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1282 can implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1xRTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

In example embodiments, the instructions 1216 are transmitted orreceived over the network 1280 using a transmission medium via a networkinterface device (e.g., a network interface component included in thecommunication components 1264) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 1216are transmitted or received using a transmission medium via the coupling1272 (e.g., a peer-to-peer coupling) to the devices 1270. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 1216for execution by the machine 1200, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium 1238 is non-transitory (inother words, not having any transitory signals) in that it does notembody a propagating signal. However, labeling the machine-readablemedium 1238 “non-transitory” should not be construed to mean that themedium is incapable of movement; the medium 1238 should be considered asbeing transportable from one physical location to another. Additionally,since the machine-readable medium 1238 is tangible, the medium 1238 maybe considered to be a machine-readable device.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: receiving, at a servercomputer system, a request for lookalike data, the request for lookalikedata comprising seed data; generating, by the server computer system,sample data from the seed data and from user data for a plurality ofusers, to use in a lookalike model training, by performing operationscomprising: generating a positive data sample from the seed data to usein the lookalike model training; and generating a negative data samplefrom user data stored in a database to use in the lookalike modeltraining; training, by the server computer system, a lookalike modelbased on the sample data and user profile features for the plurality ofusers to generate a trained lookalike model; generating, by the servercomputer system, a lookalike score for each user of the plurality ofusers in the user data using the trained lookalike model; capturing, bythe server computer system, a snapshot of social graph data for theplurality of users and determining at least one social graph feature;generating, by the server computer system, a social graph score for eachuser of the plurality of users by comparing each user of the pluralityof users to each user of the seed data based on the feature; performing,by the server computer system, optimized ranking of the users of theplurality of users based on the generated lookalike score for each userand the social graph score for each user; generating, by the servercomputer system, a final lookalike score for each user of the pluralityof users based on the optimized ranking; and generating, by the servercomputer system, a list comprising a unique identifier for each user ofthe plurality of users and an associated final lookalike score for eachunique identifier.
 2. The method of claim 1, wherein the positive datasample comprises the seed data.
 3. The method of claim 1, wherein thenegative data sample comprises a subset of the user data for theplurality of users.
 4. The method of claim 1, further comprising:capturing a user profile snapshot; generating user profile feature data;and storing the user profile feature data.
 5. The method of claim 1,wherein the request further comprises filter characteristics, and themethod further comprises: filtering the list comprising each user and anassociated final lookalike score for each user, based on filtercharacteristics received in the request for lookalike data; andassociating the filtered list with the request for lookalike data. 6.The method of claim 1, wherein the seed data comprises a plurality ofuser identifiers.
 7. The method of claim 1, wherein the generated listcomprising a unique identifier for each user of the plurality of usersand an associated final lookalike score for each unique identifier, isof a size indicated by the request for the lookalike data.
 8. The methodof claim 1, wherein the plurality of users are users of a messagingsystem or social networking system.
 9. The method of claim 1, whereinthe generated list is associated with a requester that sent the requestfor the lookalike data, and the method further comprises: receivingcontent from the requester; and displaying the content to one or moreusers of the plurality of users, based on the generated list.
 10. Aserver computer comprising: one or more hardware processors; and acomputer-readable medium coupled with the one or more hardwareprocessors, the computer-readable medium comprising instructions storedthereon that are executable by the one or more hardware processors tocause the server computer to perform operations comprising: receiving arequest for lookalike data, the request for lookalike data comprisingseed data; generating sample data from the seed data and from user datafor a plurality of users, to use in a lookalike model training, byperforming operations comprising: generating a positive data sample fromthe seed data to use in the look like model training; and generating anegative data sample from user data stored in a database to use in thelookalike model training; training a lookalike model based on the sampledata and user profile features for the plurality of users to generate atrained lookalike model; generating a lookalike score for each user ofthe plurality of users in the user data using the trained lookalikemodel; capturing a snapshot of social graph data for the plurality ofusers and determining at least one social graph feature; generating asocial graph score for each user of the plurality of users by comparingeach user of the plurality of users to each user of the seed data basedon the feature; performing optimized ranking of the users of theplurality of users based on the generated lookalike score for each userand the social graph score for each user; generating a final lookalikescore for each user of the plurality of users based on the optimizedranking; and generating a list comprising a unique identifier for eachuser of the plurality of users and an associated final lookalike scorefor each unique identifier.
 11. The server computer of claim 10, whereinthe positive data sample comprises the seed data.
 12. The servercomputer of claim 10, wherein the negative data sample comprises asubset of the user data for the plurality of users.
 13. The servercomputer of claim 10, the operations further comprising: capturing auser profile snapshot; generating user profile feature data; and storingthe user profile feature data.
 14. The server computer of claim 10,wherein the request further comprises filter characteristics, and theoperations further comprise: filtering the list comprising each user andan associated final lookalike score for each user, based on filtercharacteristics received in the request for lookalike data; andassociating the filtered list with the request for lookalike data. 15.The server computer of claim 10, wherein the seed data comprises aplurality of user identifiers.
 16. The server computer of claim 10,wherein the generated list comprising a unique identifier for each userof the plurality of users and an associated final lookalike score foreach unique identifier, is of a size indicated by the request for thelookalike data.
 17. The server computer of claim 10, wherein theplurality of users are users of a messaging system or social networkingsystem.
 18. The server computer of claim 10, wherein the generated listis associated with a requester that sent the request for the lookalikedata, and the method further comprises: receiving content from therequester; and displaying the content to one or more users of theplurality of users, based on the generated list.
 19. A non-transitorycomputer-readable medium comprising instructions stored thereon that areexecutable by at least one processor to cause a computing device toperform operations comprising: receiving a request for lookalike data,the request for lookalike data comprising seed data; generating sampledata from the seed data and from user data for a plurality of users, touse in a lookalike model training, by performing operations comprising:generating a positive data sample from the seed data to use in thelookalike model training; and generating a negative data sample fromuser data stored in a database to use in the lookalike model training;training a lookalike model based on the sample data and user profilefeatures for the plurality of users to generate a trained lookalikemodel; generating a lookalike score for each user of the plurality ofusers in the user data using the trained lookalike model; capturing asnapshot of social graph data for the plurality of users and determiningat least one social graph feature; generating a social graph score foreach user of the plurality of users by comparing each user of theplurality of users to each user of the seed data based on the feature;performing optimized ranking of the users of the plurality of usersbased on the generated lookalike score for each user and the socialgraph score for each user; generating a final lookalike score for eachuser of the plurality of users based on the optimized ranking; andgenerating a list comprising a unique identifier for each user of theplurality of users and an associated final lookalike score for eachunique identifier.
 20. The non-transitory computer-readable medium ofclaim 19, wherein the positive data sample comprises the seed data andthe negative data sample comprises a subset of the user data for theplurality of users.